Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103025
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorKazemi, SMRen_US
dc.creatorBidgoli, BMen_US
dc.creatorShamshirband, Sen_US
dc.creatorKarimi, SMen_US
dc.creatorGhorbani, MAen_US
dc.creatorChau, KWen_US
dc.creatorPour, RKen_US
dc.date.accessioned2023-11-27T06:03:57Z-
dc.date.available2023-11-27T06:03:57Z-
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/103025-
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.rights© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication S. M. R. Kazemi, Behrouz Minaei Bidgoli, Shahaboddin Shamshirband, Seyed Mehdi Karimi, Mohammad Ali Ghorbani, Kwok-wing Chau & Reza Kazem Pour (2018) Novel genetic-based negative correlation learning for estimating soil temperature, Engineering Applications of Computational Fluid Mechanics, 12:1, 506-516 is available at https://doi.org/10.1080/19942060.2018.1463871.en_US
dc.subjectDaily soil temperatureen_US
dc.subjectEstimationen_US
dc.subjectGenetic algorithmen_US
dc.subjectNegative correlation learningen_US
dc.subjectNeural network ensemble modelen_US
dc.titleNovel genetic-based negative correlation learning for estimating soil temperatureen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage506en_US
dc.identifier.epage516en_US
dc.identifier.volume12en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2018.1463871en_US
dcterms.abstractA genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to train the GNNE parameters. CLMS algorithm is used to achieve the optimum weights of components. Recorded data for two different stations located in Iran are used for the development of the GNNE models. Furthermore, the GNNE predictions are compared with the existing machine-learning models. The results demonstrate that GNNE outperforms other methods for the prediction of DSTs.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2018, v. 12, no. 1, p. 506-516en_US
dcterms.isPartOfEngineering applications of computational fluid mechanicsen_US
dcterms.issued2018-
dc.identifier.scopus2-s2.0-85056035756-
dc.identifier.eissn1997-003Xen_US
dc.description.validate202311 bcchen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Others-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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